The optimistic perspective has received only sporadic attention, with much of it centered on statistical learning. In [1]}, both pessimistic and optimistic disturbances are simultaneously considered to form optimization problems that are nonconvex, but nonetheless have structure that remains computationally tractable. This unpublished manuscript notes that nonconvex regularization and other practices for dealing with outliers have connections to such formulations; see also the recent preprint [2]} for further connections with robust statistics. In [3]}, the task of likelihood approximation in a Bayesian setting is framed as problems also involving optimization over probability measures constrained by KL-divergences, moment constraints, or Wasserstein distances. The performance of optimistic formulations with KL-divergence penalties may exceed that of solutions from sample average approximations [4]}. Additional optimistic formulations emerge in reinforcement learning [5]}, [6]} and in decision-making involving covariate information [7]}.
